Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Variability: Analysis01:11

Variability: Analysis

Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
Empirical Method to Interpret Standard Deviation01:09

Empirical Method to Interpret Standard Deviation

The empirical rule, also known as the three-sigma rule, allows a statistician to interpret the standard deviation in a normally distributed dataset. The rule states that 68% of the data lies within one standard deviation from the mean, 95% lies within two standard deviations from the mean, and 99.7% lies within three standard deviations from the mean. Additionally, this rule is also called the 68-95-99.7 rule.
This rule is used widely in statistics to calculate the proportion of data values...
Estimation of the Physical Quantities01:05

Estimation of the Physical Quantities

On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the Guinness...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Promera: a unified model for biomolecular structure prediction, filtering, and design.

bioRxiv : the preprint server for biology·2026
Same author

Thousandfold Expansion Microscopy.

bioRxiv : the preprint server for biology·2026
Same author

SwitchCraft: A Programmatic Framework for Designing State-Switching Proteins.

ArXiv·2026
Same author

Multi-resolution modeling of a discrete stochastic process identifies causes of cancer.

... International Conference on Learning Representations·2026
Same author

AI-based methods for simulating, sampling, and predicting protein ensembles.

Current opinion in structural biology·2026
Same author

Constrained Diffusion as a Paradigm for Evolution.

bioRxiv : the preprint server for biology·2026
Same journal

A k-mer-based estimator of the substitution rate between repetitive sequences.

Algorithms for molecular biology : AMB·2026
Same journal

Haplotype-aware long-read error correction.

Algorithms for molecular biology : AMB·2026
Same journal

Extension of partial atom-to-atom maps: uniqueness and algorithms.

Algorithms for molecular biology : AMB·2026
Same journal

Lossless pangenome indexing using tag arrays.

Algorithms for molecular biology : AMB·2026
Same journal

Dolphyin: a combinatorial algorithm for identifying 1-Dollo phylogenies in cancer.

Algorithms for molecular biology : AMB·2026
Same journal

Probing transcription factor subsets in gene regulatory networks.

Algorithms for molecular biology : AMB·2026
See all related articles

Related Experiment Video

Updated: Jun 2, 2026

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Sparse estimation for structural variability.

Raghavendra Hosur1, Rohit Singh, Bonnie Berger

  • 1Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA. bab@mit.edu.

Algorithms for Molecular Biology : AMB
|April 21, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to identify true protein conformational changes from noisy X-ray crystallography data. The algorithm accurately distinguishes genuine variability, aiding in understanding protein function.

More Related Videos

Data Acquisition Protocol for Determining Embedded Sensitivity Functions
07:46

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Published on: April 20, 2016

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section
11:00

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section

Published on: July 19, 2016

Related Experiment Videos

Last Updated: Jun 2, 2026

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Data Acquisition Protocol for Determining Embedded Sensitivity Functions
07:46

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Published on: April 20, 2016

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section
11:00

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section

Published on: July 19, 2016

Area of Science:

  • Structural Biology
  • Computational Biology
  • Biophysics

Background:

  • Proteins are dynamic molecules with conformational changes crucial for their function.
  • Distinguishing biologically relevant protein motions from experimental noise is a significant challenge in structural biology.

Purpose of the Study:

  • To develop a novel computational approach for identifying true conformational variability in protein structures.
  • To accurately differentiate genuine protein dynamics from noise in X-ray crystallography data.

Main Methods:

  • Inferred an ensemble of protein structures from experimental data.
  • Formulated the problem of identifying true variability as a sparse estimation problem.
  • Validated the approach using NMR experiments and synthetic data.

Main Results:

  • The developed algorithm accurately distinguishes genuine conformational changes from noise.
  • The method demonstrates improved performance and robustness compared to existing techniques.
  • Identified functionally important conformational variability in Human Ubiquitin-conjugating enzyme Ubc9.

Conclusions:

  • The novel sparse estimation approach effectively elucidates protein conformational variability from crystallographic data.
  • The algorithm's ability to identify true biological motions has implications for understanding protein function.
  • This method is robust to noise and can be integrated into existing structure-inference software.